A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products

no code implementations • LREC 2018 • Saskia Schön, Veselina Mironova, Aleksandra Gabryszak, Leonhard Hennig

Recognizing non-standard entity types and relations, such as B2B products, product classes and their producers, in news and forum texts is important in application areas such as supply chain monitoring and market research.

named-entity-recognition Named Entity Recognition +3

A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events

no code implementations • LREC 2018 • Martin Schiersch, Veselina Mironova, Maximilian Schmitt, Philippe Thomas, Aleksandra Gabryszak, Leonhard Hennig

Monitoring mobility- and industry-relevant events is important in areas such as personal travel planning and supply chain management, but extracting events pertaining to specific companies, transit routes and locations from heterogeneous, high-volume text streams remains a significant challenge.

Management named-entity-recognition +3

Annotating Relation Mentions in Tabloid Press

no code implementations • LREC 2014 • Hong Li, Sebastian Krause, Feiyu Xu, Hans Uszkoreit, Robert Hummel, Veselina Mironova

The current corpus is already in active use in our research for evaluation of the relation extraction performance of our automatically learned extraction patterns.

Relation Relation Extraction +1